Measurements of heart rate and its variability are well known in the art for their usefulness in assessing the conditions of the cardiac and the autonomic nervous systems (ANS) in both health and in disease. They are useful for monitoring many chronic diseases, such as diabetes and heart failure, as well as for monitoring cardiac status during exercise. Particularly useful is Heart Rate Variability (HRV) analysis, which is a non-invasive, clinical tool for assessing the autonomic regulation of cardiac activity as well as various autonomic-related conditions. The ANS has sympathetic and parasympathetic components. The separate rhythmic contributions from sympathetic and parasympathetic autonomic activity modulate heart rate, and thus the R-R intervals of the QRS complex in the electrocardiogram (ECG), at distinct frequencies. In humans, sympathetic activity is associated with the low frequency range (0.04-0.15 Hz) while parasympathetic activity is associated with the higher frequency range (0.15-0.4 Hz.) of the heart rate. This difference in frequency ranges allows HRV analysis to separate sympathetic and parasympathetic contributions.
educed HRV has been associated with such problems as higher long-term risk of post-infarction mortality while changes in the magnitude of, and balance between the two major components of the ANS (the sympathetic and the parasympathetic nervous systems) have been associated with diabetic neuropathy, sleep apnea, syncope and epilepsy.
Such HRV analysis has heretofore typically been performed by monitoring a subject's heart activity and storing the data from the monitored heart activity. The heart activity is monitored for several minutes to several hours. HRV analysis is commonly performed by measuring the beat-to-beat interval between successive heartbeats as collected on an electrocardiogram (ECG). A particularly useful parameter is the period between succeeding “R” waves (the RR interval), where “R” is the conventional designation given the waveform peak of a normal heartbeat as illustrated in FIG. 1. The data are transferred to a computer in which they are analyzed to provide the investigator with information such as the BPM (beats per minute [Heart Rate or Pulse Rate]), SDNN (standard deviation of RR intervals [or inter-pulse intervals] derived from the electrocardiogram [or pulse] data after putative abnormal RR intervals [or inter-pulse intervals] are removed), and RMSSD (root-mean-square of the difference between successive RR intervals [or inter-pulse intervals] derived from the electrocardiogram [or pulse] data). The generated information is reviewed by the investigator, typically long after the heart activity which was used to generate the information has taken place, and the investigator uses the generated information at that later time to determine a status or, in the case of a physician, to develop a treatment procedure for the patient. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation, 93(5), pp 1043-1065, 1996; Goldberger A L, Amaral L A N, Glass L, Hausdorff J M, Ivanov PCh, Mark R G, Mietus J E, Moody G B, Peng C K, Stanley H E. PhysioBank, PhysioToolkit, and Physionet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23): e215-e220 and U.S. Pat. Nos. 5,265,617, 5,437,285, 5,682,901, 5,842,997, 5,957,855, 6,115,629, 6,416,471, 6,480,733, and 6,485,416 variously teach HR monitoring and analysis, and their full disclosures are hereby incorporated by reference.
Many analyses of short-term electrocardiograms use conventional frequency domain HRV techniques (e.g., power spectral density) that assume “stationarity” of the underlying RR interval time series. However, most physiological signals, including heart rate (HR) and pulse rate (PR), are non-stationary by nature. This non-stationarity is a result of complex dynamic interactions among multiple bioregulatory control mechanisms responsible for maintaining homeostasis in the presence of constantly varying physiological and environmental inputs. Additionally, conventional spectral analysis methods are limited by their inability to assess transient changes in HR and PR associated with autonomic reflexes and many rapid changes induced by temporary physical or mental stresses, cardiac, or autonomic nervous system pathologies.
Joint time-frequency (t-f) signal processing techniques may be advantageously used over conventional tools for HRV analysis, given their ability to analyze time-varying spectral properties of non-stationary signals such as HRV. Such t-f techniques are ideally suited for time-localized spectral characteristics of transient cardiac events which occur as a result of temporal changes in the sympatho-vagal activities and balance. The common use of the Gabor spectrogram, where a Fourier transform is calculated for a short-time window chosen to be appropriate for the data to be collected, may make it difficult to achieve an appropriate compromise between frequency resolution and time resolution, especially at times approaching the period of the underlying oscillations.
As noted, physiological systems and their functions continuously respond to challenges. The heart rate varies from beat-to-beat. Such variations are due at least in part to the rhythmic modulation of the heart rate by the autonomic nervous system. However, the assessment of the autonomic nervous system's behavior from a single analysis of HRV can be very misleading. Assessment of the behavior of the autonomic nervous system requires that the HRV data are not based on just a single measurement, but rather that the time course of the behavior of each of the parasympathetic and sympathetic indices must be calculated within a time frame small enough to resolve the temporal nature of the physiological process under investigation.
Thus, to derive the instantaneous responses of autonomic function embedded in the spectral contents of the HRV, the time window needs to be optimal to capture these transient responses. If the window of observation is too short, the broad band white noise embedded in the spectral contents of the HRV will suppress the signals. If the window of observation is too long, the instantaneous responses will be buried in the analysis. In humans or large animals such as primates and dogs, the heart beat averages one to two beats/second, as compared to smaller animals such as the mouse, which averages 10 beats/second. Thus, it should be appreciated that an optimal timing window exists for this type of nonstationary and nonlinear analyses for different average heart rates, with the maximum size of the window dependent on the frequency components of the HRV spectrum selected for analysis.
Techniques such as chaotic analysis have the ability to assess non-linear, spatio-temporal behavior of such deterministic systems as cardiac activity. Additionally, chaotic analysis has the potential for predictive value in the screening of patients susceptible to lethal arrhythmias. A “chaotic Index” (the largest Lyapunov exponent [measure of degree of chaos] can be calculated using the data represented by the heart rate [or pulse rate] sequence. This numerical “chaotic index” can be used to quantify the degree of non-linear deterministic behavior of cardiac activity. Techniques developed out of chaos theory, such as embedding methods and estimation of Lyapunov exponents, help to unravel the original signal underlying an observed single-variable time series and determine how far into the future it can be predicted. Chaotic systems comprise a class of signals that lies between predictable periodic or quasi-periodic signals and totally irregular stochastic signals which are completely unpredictable. The Lyapunov exponent measures the sensitivity of the system to initial conditions and thus provides a measure to help predict the short-term behavior of the system. The computation of the Lypunov exponent is computationally expensive and time consuming and, until the advent of the said invention, not available for such small times.
As noted, most of the HRV analyses, including chaotic indices, are performed using prerecorded ECG data. Although such an approach has value in the treatment of a patient, the delay in the analyzed data provided to the investigator has clear disadvantages. For example, the receipt of analyzed data by a physician may be so delayed as to cause the initiation of treatment to be disadvantageously delayed. In the worst case, the information may be generated too late to be of help in treating the patient. Also, the review of such information by a clinician hours after the data were collected may make it difficult to correlate the data with other conditions of the patient for which data were not being simultaneously recorded or observed. Also, even when the patient is under observation, the clinician may be unable to temporally correlate many of those observations with the corresponding HRV data. Still further, it should be appreciated that prior art HRV information which has been generated based on a preselected set of data presents only a static picture of a dynamic situation.
The present invention is directed toward overcoming one or more of the problems set forth above.